Coronavirus disease 2019 (COVID-19) is raging worldwide. This potentially fatal infectious disease is caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). However, the complete mechanism of COVID-19 is not well understood. Therefore, we analyzed gene expression profiles of COVID-19 patients to identify disease-related genes through an innovative machine learning method that enables a data-driven strategy for gene selection from a data set with a small number of samples and many candidates. Principal-component-analysis-based unsupervised feature extraction (PCAUFE) was applied to the RNA expression profiles of 16 COVID-19 patients and 18 healthy control subjects. The results identified 123 genes as critical for COVID-19 progression from 60,683 candidate probes, including immune-related genes. The 123 genes were enriched in binding sites for transcription factors NFKB1 and RELA, which are involved in various biological phenomena such as immune response and cell survival: the primary mediator of canonical nuclear factor-kappa B (NF- κ B) activity is the heterodimer RelA-p50. The genes were also enriched in histone modification H3K36me3, and they largely overlapped the target genes of NFKB1 and RELA. We found that the overlapping genes were downregulated in COVID-19 patients. These results suggest that canonical NF- κ B activity was suppressed by H3K36me3 in COVID-19 patient blood.
【저자키워드】 machine learning, Computational science, 【초록키워드】 COVID-19, coronavirus disease, SARS-CoV-2, Coronavirus disease 2019, coronavirus, immune response, Infectious disease, severe acute respiratory syndrome Coronavirus, binding site, respiratory, Critical, mechanism, COVID-19 patients, Blood, Target genes, Gene expression profiles, COVID-19 patient, transcription factor, transcription factors, data set, target gene, mediator, number of samples, COVID-19 progression, overlapping, Candidates, acute respiratory syndrome, acute respiratory syndrome coronavirus, acute respiratory syndrome coronavirus 2, profile, healthy control, probes, NFkB1, gene expression profile, Modification, RELA, RNA expression, nuclear, cell survival, immune-related genes, Complete, data-driven, Cell, analyzed, identify, caused, involved, applied, subjects, suppressed, downregulated, canonical, biological phenomena, number of sample, overlapped, 【제목키워드】 Gene expression analysis, COVID-19 patient,